DETECCIÓN DE VEHÍCULOS¶
Definiendo funciones y librerías¶
In [1]:
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
from skimage.feature import hog
from sklearn.model_selection import train_test_split
from sklearn.svm import LinearSVC
from sklearn.preprocessing import StandardScaler
from skimage.feature import hog
import glob
import time
import cv2
%matplotlib inline
In [2]:
# Función para extraer caracterÃsticas de una lista de imágenes
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
hist_bins=32, orient=9,
pix_per_cell=8, cell_per_block=2, hog_channel=0,
spatial_feat=True, hist_feat=True, hog_feat=True):
features = []
for file in imgs:
file_features = []
image = mpimg.imread(file)
# conversión de color
if color_space != 'RGB':
if color_space == 'HSV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
elif color_space == 'LUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
elif color_space == 'HLS':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
elif color_space == 'YUV':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
elif color_space == 'YCrCb':
feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
else: feature_image = np.copy(image)
if spatial_feat == True:
spatial_features = bin_spatial(feature_image, size=spatial_size)
file_features.append(spatial_features)
if hist_feat == True:
hist_features = color_hist(feature_image, nbins=hist_bins)
file_features.append(hist_features)
if hog_feat == True:
if hog_channel == 'ALL':
hog_features = []
for channel in range(feature_image.shape[2]):
hog_features.append(get_hog_features(feature_image[:,:,channel],
orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True))
hog_features = np.ravel(hog_features)
else:
hog_features = get_hog_features(feature_image[:,:,hog_channel], orient,
pix_per_cell, cell_per_block, vis=False, feature_vec=True)
#
file_features.append(hog_features)
features.append(np.concatenate(file_features))
return features
def get_hog_features(img, orient, pix_per_cell, cell_per_block,
vis=False, feature_vec=True):
if vis == True:
features, hog_image = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=False,
visualize=vis, feature_vector=feature_vec)
return features, hog_image
else:
features = hog(img, orientations=orient,
pixels_per_cell=(pix_per_cell, pix_per_cell),
cells_per_block=(cell_per_block, cell_per_block),
transform_sqrt=False,
visualize=vis, feature_vector=feature_vec)
return features
def bin_spatial(img, size=(32, 32)):
color1 = cv2.resize(img[:,:,0], size).ravel()
color2 = cv2.resize(img[:,:,1], size).ravel()
color3 = cv2.resize(img[:,:,2], size).ravel()
return np.hstack((color1, color2, color3))
def color_hist(img, nbins=32): #bins_range=(0, 256)
# Se calcula el histograma de los canales de color por separado.
channel1_hist = np.histogram(img[:,:,0], bins=nbins)
channel2_hist = np.histogram(img[:,:,1], bins=nbins)
channel3_hist = np.histogram(img[:,:,2], bins=nbins)
# Se concatenan los histogramas en un solo vector de caracterÃsticas
hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
return hist_features
Recopilación de datos¶
In [5]:
# Obtenemos las imágenes del dataset
images = glob.glob('./training-data/*/*/*.png')
cars = []
notcars = []
all_cars = []
all_notcars = []
for image in images:
if 'non-vehicles' in image:
all_notcars.append(image)
else:
all_cars.append(image)
for ix, notcar in enumerate(all_notcars):
if ix % 5 == 0:
notcars.append(notcar)
for ix, car in enumerate(all_cars):
if ix % 5 == 0:
cars.append(car)
# Imprimimos 2 imágenes aleatorias para ver si el aprendizaje está dando buenos resultados
car_image = mpimg.imread(cars[50])
notcar_image = mpimg.imread(notcars[20])
def compare_images(image1, image2, image1_exp="Image 1", image2_exp="Image 2"):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
f.tight_layout()
ax1.imshow(image1)
ax1.set_title(image1_exp, fontsize=20)
ax2.imshow(image2)
ax2.set_title(image2_exp, fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
compare_images(car_image, notcar_image, "Carro", "No hay carro")
Extrayendo Características¶
In [6]:
color_space = 'YUV'
orient = 15
pix_per_cell = 8
cell_per_block = 2
hog_channel = "ALL"
spatial_size = (32, 32)
hist_bins = 32
spatial_feat = True
hist_feat = True
hog_feat = True
converted_car_image = cv2.cvtColor(car_image, cv2.COLOR_RGB2YUV)
car_ch1 = converted_car_image[:,:,0]
car_ch2 = converted_car_image[:,:,1]
car_ch3 = converted_car_image[:,:,2]
converted_notcar_image = cv2.cvtColor(notcar_image, cv2.COLOR_RGB2YUV)
notcar_ch1 = converted_notcar_image[:,:,0]
notcar_ch2 = converted_notcar_image[:,:,1]
notcar_ch3 = converted_notcar_image[:,:,2]
car_hog_feature, car_hog_image = get_hog_features(car_ch1,
orient, pix_per_cell, cell_per_block,
vis=True, feature_vec=True)
notcar_hog_feature, notcar_hog_image = get_hog_features(notcar_ch1,
orient, pix_per_cell, cell_per_block,
vis=True, feature_vec=True)
car_ch1_features = cv2.resize(car_ch1, spatial_size)
car_ch2_features = cv2.resize(car_ch2, spatial_size)
car_ch3_features = cv2.resize(car_ch3, spatial_size)
notcar_ch1_features = cv2.resize(notcar_ch1, spatial_size)
notcar_ch2_features = cv2.resize(notcar_ch2, spatial_size)
notcar_ch3_features = cv2.resize(notcar_ch3, spatial_size)
def show_images(image1, image2, image3, image4, image1_exp="Image 1", image2_exp="Image 2", image3_exp="Image 3", image4_exp="Image 4"):
f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image1)
ax1.set_title(image1_exp, fontsize=20)
ax2.imshow(image2)
ax2.set_title(image2_exp, fontsize=20)
ax3.imshow(image3)
ax3.set_title(image3_exp, fontsize=20)
ax4.imshow(image4)
ax4.set_title(image4_exp, fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
show_images(car_ch1, car_hog_image, notcar_ch1, notcar_hog_image, "Car ch 1", "Car ch 1 HOG", "Not Car ch 1", "Not Car ch 1 HOG")
show_images(car_ch1, car_ch1_features, notcar_ch1, notcar_ch1_features, "Car ch 1", "Car ch 1 features", "Not Car ch 1", "Not Car ch 1 features")
show_images(car_ch2, car_ch2_features, notcar_ch2, notcar_ch2_features, "Car ch 2", "Car ch 2 features", "Not Car ch 2", "Not Car ch 2 features")
show_images(car_ch3, car_ch3_features, notcar_ch3, notcar_ch3_features, "Car ch 3", "Car ch 3 features", "Not Car ch 3", "Not Car ch 3 features")
Clasificador de entrenamiento¶
In [7]:
car_features = extract_features(cars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(notcars, color_space=color_space,
spatial_size=spatial_size, hist_bins=hist_bins,
orient=orient, pix_per_cell=pix_per_cell,
cell_per_block=cell_per_block,
hog_channel=hog_channel, spatial_feat=spatial_feat,
hist_feat=hist_feat, hog_feat=hog_feat)
X = np.vstack((car_features, notcar_features)).astype(np.float64)
X_scaler = StandardScaler().fit(X)
scaled_X = X_scaler.transform(X)
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
scaled_X, y, test_size=0.2, random_state=rand_state)
print('Using:',orient,'orientations',pix_per_cell,
'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))
# Uso del SVC lineal
svc = LinearSVC()
# tiempo de entrenamiento
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# puntaje obtenido
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# tiempo de predicción para una sola muestra
t=time.time()
Realizar la detección en las imágenes¶
In [9]:
def convert_color(img, conv='RGB2YCrCb'):
if conv == 'RGB2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
if conv == 'BGR2YCrCb':
return cv2.cvtColor(img, cv2.COLOR_BGR2YCrCb)
if conv == 'RGB2LUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
if conv == 'RGB2YUV':
return cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
def find_cars(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
draw_img = np.copy(img)
img = img.astype(np.float32)/255
img_tosearch = img[ystart:ystop,:,:] # sub-sampling
ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YUV')
if scale != 1:
imshape = ctrans_tosearch.shape
ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
ch1 = ctrans_tosearch[:,:,0]
ch2 = ctrans_tosearch[:,:,1]
ch3 = ctrans_tosearch[:,:,2]
nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1
nfeat_per_block = orient*cell_per_block**2
window = 64
nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
cells_per_step = 2
nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
nysteps = (nyblocks - nblocks_per_window) // cells_per_step
hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=False)
hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=False)
hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=False)
bboxes = []
for xb in range(nxsteps):
for yb in range(nysteps):
ypos = yb*cells_per_step
xpos = xb*cells_per_step
hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel()
hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))
xleft = xpos*pix_per_cell
ytop = ypos*pix_per_cell
subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
spatial_features = bin_spatial(subimg, size=spatial_size)
hist_features = color_hist(subimg, nbins=hist_bins)
test_stacked = np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1)
test_features = X_scaler.transform(test_stacked)
test_prediction = svc.predict(test_features)
if test_prediction == 1:
xbox_left = np.int(xleft*scale)
ytop_draw = np.int(ytop*scale)
win_draw = np.int(window*scale)
cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6)
bboxes.append(((int(xbox_left), int(ytop_draw+ystart)),(int(xbox_left+win_draw),int(ytop_draw+win_draw+ystart))))
return draw_img, bboxes
def apply_sliding_window(image, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins):
bboxes = []
ystart = 400
ystop = 500
out_img, bboxes1 = find_cars(image, ystart, ystop, 1.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 400
ystop = 500
out_img, bboxes2 = find_cars(out_img, ystart, ystop, 1.3, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 410
ystop = 500
out_img, bboxes3 = find_cars(out_img, ystart, ystop, 1.4, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 420
ystop = 556
out_img, bboxes4 = find_cars(out_img, ystart, ystop, 1.6, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 430
ystop = 556
out_img, bboxes5 = find_cars (out_img, ystart, ystop, 1.8, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 430
ystop = 556
out_img, bboxes6 = find_cars (out_img, ystart, ystop, 2.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 440
ystop = 556
out_img, bboxes7 = find_cars (out_img, ystart, ystop, 1.9, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 400
ystop = 556
out_img, bboxes8 = find_cars (out_img, ystart, ystop, 1.3, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 400
ystop = 556
out_img, bboxes9 = find_cars (out_img, ystart, ystop, 2.2, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
ystart = 500
ystop = 656
out_img, bboxes10 = find_cars (out_img, ystart, ystop, 3.0, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
bboxes.extend(bboxes1)
bboxes.extend(bboxes2)
bboxes.extend(bboxes3)
bboxes.extend(bboxes4)
bboxes.extend(bboxes5)
bboxes.extend(bboxes6)
bboxes.extend(bboxes7)
bboxes.extend(bboxes8)
bboxes.extend(bboxes9)
bboxes.extend(bboxes10)
return out_img, bboxes
image1 = mpimg.imread('./test_series/series1.jpg')
image2 = mpimg.imread('./test_series/series2.jpg')
image3 = mpimg.imread('./test_series/series3.jpg')
image4 = mpimg.imread('./test_series/series4.jpg')
image5 = mpimg.imread('./test_series/series5.jpg')
image6 = mpimg.imread('./test_series/series6.jpg')
output_image1, bboxes1 = apply_sliding_window(image1, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image2, bboxes2 = apply_sliding_window(image2, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image3, bboxes3 = apply_sliding_window(image3, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image4, bboxes4 = apply_sliding_window(image4, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image5, bboxes5 = apply_sliding_window(image5, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
output_image6, bboxes6 = apply_sliding_window(image6, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
image = mpimg.imread('./test_images/test4.jpg')
draw_image = np.copy(image)
output_image, bboxes = apply_sliding_window(image, svc, X_scaler, pix_per_cell, cell_per_block, spatial_size, hist_bins)
def show_images(image1, image2, image3, image1_exp="Image 1", image2_exp="Image 2", image3_exp="Image 3"):
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image1)
ax1.set_title(image1_exp, fontsize=20)
ax2.imshow(image2)
ax2.set_title(image2_exp, fontsize=20)
ax3.imshow(image3)
ax3.set_title(image3_exp, fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
show_images(output_image1, output_image2, output_image3)
show_images(output_image4, output_image5, output_image6)
Mapa de calor¶
In [10]:
from scipy.ndimage.measurements import label
def add_heat(heatmap, bbox_list):
for box in bbox_list:
heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1
return heatmap
def apply_threshold(heatmap, threshold):
heatmap[heatmap <= threshold] = 0
return heatmap
def draw_labeled_bboxes(img, labels):
for car_number in range(1, labels[1]+1):
nonzero = (labels[0] == car_number).nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
cv2.rectangle(img, bbox[0], bbox[1], (0,0,255), 6)
return img
heat = np.zeros_like(output_image[:,:,0]).astype(np.float)
heat = add_heat(heat, bboxes)
threshold = 1
heat = apply_threshold(heat, threshold)
# Visualizar mapa de calor
heatmap = np.clip(heat, 0, 255)
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(image), labels)
def show_images(image1, image2, image1_exp="Image 1", image2_exp="Image 2"):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image1)
ax1.set_title(image1_exp, fontsize=20)
ax2.imshow(image2, cmap='hot')
ax2.set_title(image2_exp, fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
show_images(output_image, heatmap, "Car Positions", "Result")
In [11]:
def get_heatmap(bboxes):
threshold = 1
heat = np.zeros_like(output_image[:,:,0]).astype(np.float)
heat = add_heat(heat, bboxes)
heat = apply_threshold(heat, threshold)
heatmap = np.clip(heat, 0, 255)
return heatmap
def show_images(image1, image2, image1_exp="Image 1", image2_exp="Image 2"):
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image1)
ax1.set_title(image1_exp, fontsize=20)
ax2.imshow(image2, cmap='hot')
ax2.set_title(image2_exp, fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
heatmap1 = get_heatmap(bboxes1)
heatmap2 = get_heatmap(bboxes2)
heatmap3 = get_heatmap(bboxes3)
heatmap4 = get_heatmap(bboxes4)
heatmap5 = get_heatmap(bboxes5)
heatmap6 = get_heatmap(bboxes6)
show_images(output_image1, heatmap1)
show_images(output_image2, heatmap2)
show_images(output_image3, heatmap3)
show_images(output_image4, heatmap4)
show_images(output_image5, heatmap5)
show_images(output_image6, heatmap6)
Imagen etiquetada¶
In [12]:
plt.imshow(labels[0], cmap='gray')
Out[12]:
In [13]:
plt.imshow(draw_img)
Out[13]:
In [ ]: